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This research deals with the cooperative control of CAVs (Connected and Automated Vehicles) at signal-free intersection. CAVs communicate with each other and adjust their speeds to safely pass through the intersection without having to stop. This is expected to improve intersection throughput and reduce fuel consumption. In order to execute cooperative control, a two-stage control structure is considered. In the first stage, the merging time, defined for each CAV as the time at which the CAV reaches the intersection, is obtained by solving mixed integer linear programming (MILP). In the second stage, each CAV solves an optimal control problem to determine the control input that allows it to reach the intersection at the merging time obtained in the first stage. In this research, a nonlinear model that considers air resistance, rolling resistance, and slope resistance is used to take into account the real environment. We propose a method using a control barrier function to guarantee the safety of CAVs even with nonlinear models , and an input correction method using a disturbance observer to suppress modelling errors caused by complex dynamics. We also propose a method that consists of establishing a speed regulation zone to avoid the possibility of passing through an intersection at an unsafe speed to improve throughput. Finally, a comparison with previous studies shows the superiority of the proposed method in reducing fuel consumption.

The problem of decentralized control for overall system stability has received increasing attention due to heterogeneity in power networks. To address this problem, two passivity indices are introduced to characterize the shortage of nonlinear negative imaginary property and $ L_2 $ stability of the coupling network. In a modular decomposition, novel passivity-short-based conditions in a decentralized manner are investigated to ensure asymptotically stability of the overall system and $ L_2 $ stability of the network simultaneously, which is so-called system-wide stability with $ L_2 $ gain. Furthermore, the local subsystems are equipped with a linear matrix inequality-based controller, implementing decentralized stabilization. A numerical simulation with three heterogeneous bus dynamics is illustrated to demonstrate the effectiveness of the proposed controller design considering load change and measurement errors, offering insights into system robustness.

Abstract This paper addresses the problem of distributed secure state estimation of a discrete‐time linear dynamical system in an adversarial environment. Much of the recent work on this problem has imposed redundancy requirements on measurement and communication networks for state estimation in the presence of attacks. In this paper, the authors aim to relax this redundancy requirement by using prior information about the state to be estimated. A scenario where sparsity information of the state is given is especially considered, and it is shown that the a priori sparsity information alleviates the redundancy requirement. An algorithm for distributed secure state estimation is then derived and a sufficient condition on the convergence of the algorithm is given. Numerical simulations finally illustrate the effectiveness of the algorithm.

Abstract To date, multicopters have mainly been used in the entertainment and information‐gathering fields, but can potentially be used to transport payloads. Specifically, multicopters are more suited for manoeuvring tough terrains and delivering payloads to areas unaccessible by other means. The authors propose a control method to achieve cooperative transport with a plural multicopter system and address the problem of delivering a payload being suspended via cables from multicopters. In cooperative transport using a multicopter system, disturbance (weight, wind, and inertia force) may cause destabilization. A consensus algorithm is used to coordinate flight of helicopters in a formation and to ensure that the payload follows a virtual leader. Disturbances (such as wind and cable tension) are primary causes of destabilization and, in the multicopter setting, they are suppressed by an adaptive robust control, Robust Integral of Sign of the Error control. It is shown that the proposed control design can achieve not only convergence under the ideal setting but also robust operation under significant disturbances. In addition, additional control objectives such as cable tension, payload attitude, and collision avoidance can be integrated and achieved. As an auxiliary method, cable tension control, maintaining a horizontal payload attitude, and collision avoidance are considered. The usefulness of the proposed method was verified by simulations and laboratory flight testing demonstrations.

In this paper, we aim to propose a charging scheduling algorithm for electric vehicles on highways. While the number of electric vehicles has been increasing recently, charging stations are not becoming widespread compared to gas stations. The distance that an electric vehicle can run on one charge is only around 120km to 400km. Therefore, it is necessary to plan to recharge in advance when driving long distances. Problems related to planning algorithms are called charging scheduling problems of electric vehicles. In this paper, we assume that there is no difference in the power of the electric vehicle and the charging station, and consider the situation where each acts to maximize its profit. First, since the electric vehicle can select the charging station freely, it motivates us to solve the optimal allocation problem of the electric vehicle to the charging station using matching theory. Then, non-cooperative game theory is utilized to obtain the energy demand and energy price for the electric vehicles and charging stations, respectively. In addition, the convergence condition of the non-cooperative game is theoretically derived. Finally, the effectiveness of the proposed non-cooperative charging scheduling algorithm is confirmed by numerical simulation.

This paper presents the optimal motion planning problem for connected and automated vehicles (CAVs) to cross a conflict area at an intersection with state and control constraints. First, we formulate the scheduled merging (or crossing) time for all CAVs as a mixed integer linear programming (MILP) problem where the merging time is solved frequently. Second, we formulate the optimal motion planning problem so that the CAVs can achieve their scheduled merging time as well as minimizing the energy consumption. Since we solve the motion planning problem analytically, not all the solutions are feasible to comply with the frequently updated merging time. To solve this problem, we propose a feasibility enforcement period (FEP). Then, we validate the proposed solution through simulation, and the results show that even the merging time is frequently updated, the CAVs can still achieve the merging time with a minimal deviation. Besides, our proposed framework also shows a significant improvement in terms of travel time as compared to the conventional one.

In this paper, the Alternating Direction Method of Multipliers (ADMM) is investigated for distributed optimization problems in a networked multi-agent system. In particular, a new adaptive-gain ADMM algorithm is derived in a closed form and under the standard convex property in order to greatly speed up convergence of ADMM-based distributed optimization. Using Lyapunov direct approach, the proposed solution embeds control gains into weighted network matrix among the agents and uses those weights as adaptive penalty gains in the augmented Lagrangian. It is shown that the proposed closed loop gain adaptation scheme significantly improves the convergence time of underlying ADMM optimization. Convergence analysis is provided and simulation results are included to demonstrate the effectiveness of the proposed scheme.

This paper addresses the crossing order problem for connected and automated vehicles at an intersection where the problem can lead to higher traffic congestion, especially in high traffic density. First, we formulated the problem with mixed integer linear programming where the solution yields optimal crossing time for each vehicle to cross the intersection. Then, we present an optimization framework to drive each vehicle towards the assigned crossing time while conserving the fuel consumption. Finally, we simulate the intersection scenario with the proposed solution, where it is shown that both congestion and fuel consumption can be reduced significantly.

In this study, networks of interconnected heterogeneous micro-grids are studied. The transient dynamics is modelled as an averaging process whereby micro-grids are assimilated to dynamic agents in a network. An analysis of the convergence of the consensus dynamics is carried out under different assumptions on the damping and inertia parameters and the topology of the network. This study provides an insight into the relation between the network topology and the system's response. An analysis of the ways in which the heterogeneous inertial parameters affect the transient response of the network is also implemented. Additionally, the conditions that guarantee stability are identified when the system is under the influence of uncertain non-linear parameters. Finally, simulations are carried out based on a model calibrated on an existing network in the UK under parameter uncertainties.

This paper deals with passivity-based stability analysis of electricity market trading with dynamic pricing. In the deregulated electricity market, power consumers and generators will participate in market trading as market players. For such a new kind of the market trading system, the dynamic pricing procedure has to be taken into account of the intermittent participation of power consumers and generators. Then, this paper discusses the stability of the electricity market trading system including power flow using passivity analysis. This paper also shows that the optimal power demand, supply and electricity prices are asymptotically stable and these values are derived in a distributed manner through market trading.

This paper deals with the problem of secure state estimation in an adversarial environment with the presence of bounded noises. The problem is given as min-max optimization, that is, the system operator seeks an optimal estimate which minimizes the worst-case estimation error due to the manipulation by the attacker. To derive the optimal estimate, taking the reach set of the system into account, we first show that the feasible set of the state can be represented as a union of polytopes, and the optimal estimate is given as the Chebyshev center of the union. Then, for calculating the optimal state estimate, we provide a convex optimization problem that utilizes the vertices of the union. On the proposed estimator, the estimation error is bounded even if the adversary corrupts any subset of sensors. For the sake of reducing the calculation complexity, we further provide another estimator which resorts to the interval hull approximation of the reach set and properties of zonotopes. This approximated estimator is able to reduce the complexity without degrading the estimation accuracy sorely. Numerical comparisons and examples finally illustrate the effectiveness of the proposed estimators.

This paper deals with a demand adjustment problem of each consumer having appliances by an aggregator based on optimal pricing in a day-ahead electricity market. In this paper, we model consumers, generators, and aggregators and design a market mechanism, where they act to maximize their own profit based on the power price and decide the electricity supply and demand. The dual decomposition is applied to the market problem to maximize the social welfare, where the proposed algorithm decides an electricity price based on the exchange of information among market participants to solve each distributed problem for improving convergence. In addition, the convergence of this proposed method is proven using Lyapunov's stability theorem. Finally, simulation results show that matching of supply and demand is achieved, while satisfying constraints on the consumption amount and the use time of each appliance, and the price determined using proposed price update algorithm converges to a certain price.

In this paper, we analyze the vulnerabilities due to integrity cyber attacks named zero-stealthy attacks in cyber-physical systems, which are modeled as a stochastic linear time invariant (LTI) system equipped with a Kalman filter, an LQG controller, and a χ2 failure detector. The attacks are designed by a sophisticated attacker so that the measurement residual of the compromised system coincides with the healthy one, and thus it is impossible to detect the attacks. First, we characterize and analyze an existence condition of the attacks from an attacker's standpoint. Then, we extend the attacks into an attacker's goal: The scenario when the adversary wishes to manipulate the systems to an objective designed by him/her. Our results provide that the attacker can manipulate the compromised system to the objective without accessing the networks of real-time sensor or actuator data. Finally, we verify the dangerousness of the attacks through a simple numerical example.